// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

template<typename DataType, int DataLayout, typename IndexType>
static void
test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
{
	IndexType sizeDim1 = 2;
	IndexType sizeDim2 = 3;
	IndexType sizeDim3 = 5;
	IndexType sizeDim4 = 7;
	array<IndexType, 4> tensorRange = { { sizeDim1, sizeDim2, sizeDim3, sizeDim4 } };
	Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
	Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
	tensor.setRandom();

	const size_t buffSize = tensor.size() * sizeof(DataType);
	array<IndexType, 4> shuffles;
	shuffles[0] = 0;
	shuffles[1] = 1;
	shuffles[2] = 2;
	shuffles[3] = 3;
	DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
	DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));

	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);

	sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);

	gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
	sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
	sycl_device.synchronize();

	VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
	VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
	VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
	VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);

	for (IndexType i = 0; i < sizeDim1; ++i) {
		for (IndexType j = 0; j < sizeDim2; ++j) {
			for (IndexType k = 0; k < sizeDim3; ++k) {
				for (IndexType l = 0; l < sizeDim4; ++l) {
					VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
				}
			}
		}
	}

	shuffles[0] = 2;
	shuffles[1] = 3;
	shuffles[2] = 1;
	shuffles[3] = 0;
	array<IndexType, 4> tensorrangeShuffle = { { sizeDim3, sizeDim4, sizeDim2, sizeDim1 } };
	Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
	DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(gpu_data3, tensorrangeShuffle);

	gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
	sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
	sycl_device.synchronize();

	VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
	VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
	VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
	VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);

	for (IndexType i = 0; i < sizeDim1; ++i) {
		for (IndexType j = 0; j < sizeDim2; ++j) {
			for (IndexType k = 0; k < sizeDim3; ++k) {
				for (IndexType l = 0; l < sizeDim4; ++l) {
					VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
				}
			}
		}
	}
}

template<typename DataType, typename dev_Selector>
void
sycl_shuffling_test_per_device(dev_Selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
	test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
	}
}
